Objective evaluation of expert and novice performance during robotic surgical training tasks

Using neuroimaging to assess brain activity and areas associated with surgical skills: a systematic review

BACKGROUND

Surgical skills acquisition is under continuous development due to the emergence of new technologies, and there is a need for assessment tools to develop along with these. A range of neuroimaging modalities has been used to map the functional activation of brain networks while surgeons acquire novel surgical skills. These have been proposed as a method to provide a deeper understanding of surgical expertise and offer new possibilities for the personalized training of future surgeons. With studies differing in modalities, outcomes, and surgical skills there is a need for a systematic review of the evidence. This systematic review aims to summarize the current knowledge on the topic and evaluate the potential use of neuroimaging in surgical education.

METHODS

We conducted a systematic review of neuroimaging studies that mapped functional brain activation while surgeons with different levels of expertise learned and performed technical and non-technical surgical tasks. We included all studies published before July 1st, 2023, in MEDLINE, EMBASE and WEB OF SCIENCE.

RESULTS

38 task-based brain mapping studies were identified, consisting of randomized controlled trials, case-control studies, and observational cohort or cross-sectional studies. The studies employed a wide range of brain mapping modalities, including electroencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy, activating brain areas involved in the execution and sensorimotor or cognitive control of surgical skills, especially the prefrontal cortex, supplementary motor area, and primary motor area, showing significant changes between novices and experts.

CONCLUSION

Functional neuroimaging can reveal how task-related brain activity reflects technical and non-technical surgical skills. The existing body of work highlights the potential of neuroimaging to link task-related brain activity patterns with the individual level of competency or improvement in performance after training surgical skills. More research is needed to establish its validity and usefulness as an assessment tool.

Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND

There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula.

METHODS

A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias.

RESULTS

Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent.

CONCLUSIONS

Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901.

An Innovative Comparative Analysis Approach for the Assessment of Laparoscopic Surgical Skills

Over the past few decades, surgeon training has changed dramatically. Surgical skills are now taught in a surgical skills laboratory instead of the operating room. Simulation-based training helps medical students improve their skills, but it has not revolutionized clinical education. One critical barrier to reaching such a desired goal is the lack of reliable, robust, and objective methods for assessing the effectiveness of training sessions and the development of students. In this paper, we will develop a new comparative analysis approach that employs network models as the central concept in establishing a new assessment tool for the evaluation of the surgical skills of trainees as well as the training processes. The model is populated using participants electromyography data while performing a simulation task. Furthermore, using NASA Task Load Index score, participants’ subjective overload levels are analyzed to examine the impact of participants’ perception of their mental demand, physical demand, temporal demand, performance, effort, and frustration on how participants perform each simulation task. Obtained results indicate that the proposed approach enables us to extract useful information from the raw data and provides an objective method for assessment the of surgical simulation tasks and how the participants’ perception of task impacts their performance.

Quantifying the Impact of Signal-to-background Ratios on Surgical Discrimination of Fluorescent Lesions

PURPOSE

Surgical fluorescence guidance has gained popularity in various settings, e.g., minimally invasive robot-assisted laparoscopic surgery. In pursuit of novel receptor-targeted tracers, the field of fluorescence-guided surgery is currently moving toward increasingly lower signal intensities. This highlights the importance of understanding the impact of low fluorescence intensities on clinical decision making. This study uses kinematics to investigate the impact of signal-to-background ratios (SBR) on surgical performance.

METHODS

Using a custom grid exercise containing hidden fluorescent targets, a da Vinci Xi robot with Firefly fluorescence endoscope and ProGrasp and Maryland forceps instruments, we studied how the participants' (N = 16) actions were influenced by the fluorescent SBR. To monitor the surgeon's actions, the surgical instrument tip was tracked using a custom video-based tracking framework. The digitized instrument tracks were then subjected to multi-parametric kinematic analysis, allowing for the isolation of various metrics (e.g., velocity, jerkiness, tortuosity). These were incorporated in scores for dexterity (Dx), decision making (DM), overall performance (PS) and proficiency. All were related to the SBR values.

RESULTS

Multi-parametric analysis showed that task completion time, time spent in fluorescence-imaging mode and total pathlength are metrics that are directly related to the SBR. Below SBR 1.5, these values substantially increased, and handling errors became more frequent. The difference in Dx and DM between the targets that gave SBR < 1.50 and SBR > 1.50, indicates that the latter group generally yields a 2.5-fold higher Dx value and a threefold higher DM value. As these values provide the basis for the PS score, proficiency could only be achieved at SBR > 1.55.

CONCLUSION

By tracking the surgical instruments we were able to, for the first time, quantitatively and objectively assess how the instrument positioning is impacted by fluorescent SBR. Our findings suggest that in ideal situations a minimum SBR of 1.5 is required to discriminate fluorescent lesions, a substantially lower value than the SBR 2 often reported in literature.

Where Robotic Surgery Meets the Metaverse

With a focus on hepatobiliary surgery, the review by Giannone et al [...].

Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study

INTRODUCTION

An eyeglass gaze camera and a skeletal coordinate camera without sensors attached to the operator's body were used to monitor gaze and movement during a simulated surgical procedure. These new devices have the potential to change skill assessment for laparoscopic surgery. The suitability of these devices for skill assessment was investigated.

MATERIAL AND METHODS

Six medical students, six intermediate surgeons, and four experts performed suturing tasks in a dry box. The tip positions of the instruments were identified from video recordings. Performance was evaluated based on instrument movement, gaze, and skeletal coordination.

RESULTS

Task performance time and skeletal coordinates were not significantly different among skill levels. The total movement distance of the right instrument was significantly different depending on the skill level. The SD of the gaze coordinates was significantly different depending on skill level and was less for experts. The expert's gaze stayed in a small area with little blurring.

CONCLUSIONS

The SD of gaze point coordinates correlates with laparoscopic surgical skill level. These devices may facilitate objective intraoperative skill evaluation in future studies.

Beyond Efficiency: Surface Electromyography Enables Further Insights into the Surgical Movements of Urologists

INTRODUCTION

Surgical skill evaluation while performing minimally invasive surgeries is a highly complex task. It is important to objectively assess an individual's technical skills throughout surgical training to monitor progress and to intervene when skills are not commensurate with the year of training. The miniaturization of wireless wearable platforms integrated with sensor technology has made it possible to non-invasively assess muscle activations and movement variability during performance of minimally invasive surgical tasks. Our objective was to use electromyography to deconstruct the motions of a surgeon during robotic suturing and distinguish quantifiable movements that characterize the skill of an experienced, expert urologic surgeon from trainees.

METHODS

Three skill groups of participants: novice (n=11), intermediate (n=12) and expert (n=3) were enrolled in the study. A total of 12 wireless wearable sensors consisting of surface electromyograms (EMGs) and accelerometers were placed along upper extremity muscles to assess muscle activations and movement variability, respectively. Participants then performed a robotic suturing task.

RESULTS

EMG-based parameters: total time, dominant frequency, cumulative muscular workload (CMW were significantly different across the three skill groups. We also found nonlinear movement variability parameters such as correlation dimension, Lyapunov exponent trended differently across the three skill groups.

CONCLUSIONS

These findings suggest that economy of motion variables and nonlinear movement variabilities are affected by surgical experience level. Wearable sensor signal analysis could make it possible to objectively evaluate surgical skill level periodically throughout the residency training experience.

Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention

It has been shown that intraoperative stress can have a negative effect on surgeon surgical skills during laparoscopic procedures. For novice surgeons, stressful conditions can lead to significantly higher velocity, acceleration, and jerk of the surgical instrument tips, resulting in faster but less smooth movements. However, it is still not clear which of these kinematic features (velocity, acceleration, or jerk) is the best marker for identifying the normal and stressed conditions. Therefore, in order to find the most significant kinematic feature that is affected by intraoperative stress, we implemented a spatial attention-based Long-Short-Term-Memory (LSTM) classifier. In a prior IRB approved experiment, we collected data from medical students performing an extended peg transfer task who were randomized into a control group and a group performing the task under external psychological stresses. In our prior work, we obtained "representative" normal or stressed movements from this dataset using kinematic data as the input. In this study, a spatial attention mechanism is used to describe the contribution of each kinematic feature to the classification of normal/stressed movements. We tested our classifier under Leave-One-User-Out (LOUO) cross-validation, and the classifier reached an overall accuracy of 77.11% for classifying "representative" normal and stressed movements using kinematic features as the input. More importantly, we also studied the spatial attention extracted from the proposed classifier. Velocity and acceleration on both sides had significantly higher attention for classifying a normal movement (p <= 0.0001); Velocity (p <= 0.015) and jerk (p <= 0.001) on non-dominant hand had significant higher attention for classifying a stressed movement, and it is worthy noting that the attention of jerk on non-dominant hand side had the largest increment when moving from describing normal movements to stressed movements (p = 0.0000). In general, we found that the jerk on non-dominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively.

Capturing the clinical decision-making processes of expert and novice diabetic retinal graders using a 'think-aloud' approach

BACKGROUND

Diabetic eye screening programmes have been developed worldwide based on evidence that early detection and treatment of diabetic retinopathy are crucial to preventing sight loss. However, little is known about the decision-making processes and training needs of diabetic retinal graders, particularly in low- and middle-income countries.

OBJECTIVES

To provide data for improving evidence-based diabetic retinopathy training to help novice graders process fundus images more like experts.

SUBJECTS/METHODS

This is a mixed-methods qualitative study conducted in southern Vietnam and Northern Ireland. Novice diabetic retinal graders in Vietnam (n = 18) and expert graders in Northern Ireland (n = 5) were selected through a purposive sampling technique. Data were collected from 21st February to 3rd September 2019. The interviewer used neutral prompts during think-aloud sessions to encourage participants to verbalise their thought processes while grading fundus images from anonymised patients, followed by semi-structured interviews. Thematic framework analysis was used to identify themes, supported by illustrative quotes from interviews. Mann-Whitney U tests were used to compare graders' performance.

RESULTS

Expert graders used a more systematic approach when grading images, considered all four images per patient and used available software tools such as red-free filters prior to making a decision on management. The most challenging features for novice graders were intra-retinal microvascular abnormalities and new vessels, which were more accurately identified by experts.

CONCLUSION

Taking more time to grade fundus images and adopting a protocol-driven "checklist" approach may help novice graders to function more like experts.

Identifying Kinematic Markers Associated with Intraoperative Stress during Surgical Training Tasks

Increased levels of stress can impair surgeon performance and patient safety during surgery. The aim of this study is to investigate the effect of short term stressors on laparoscopic performance through analysis of kinematic data. Thirty subjects were randomly assigned into two groups in this IRB-approved study. The control group was required to finish an extended-duration peg transfer task (6 minutes) using the FLS trainer while listening to normal simulated vital signs and while being observed by a silent moderator. The stressed group finished the same task but listened to a period of progressively deteriorating simulated patient vitals, as well as critical verbal feedback from the moderator, which culminated in 30 seconds of cardiac arrest and expiration of the simulated patient. For all subjects, video and position data using electromagnetic trackers mounted on the handles of the laparoscopic instruments were recorded. A statistical analysis comparing time-series velocity, acceleration, and jerk data, as well as path length and economy of volume was conducted. Clinical stressors lead to significantly higher velocity, acceleration, jerk, and path length as well as lower economy of volume. An objective evaluation score using a modified OSATS technique was also significantly worse for the stressed group than the control group. This study shows the potential feasibility and advantages of using the time-series kinematic data to identify the stressful conditions during laparoscopic surgery in near-real-time. This data could be useful in the design of future robot-assisted algorithms to reduce the unwanted effects of stress on surgical performance.

Properties and Characteristics of Three-Dimensional Printed Head Models Used in Simulation of Neurosurgical Procedures: A Scoping Review

BACKGROUND

Intracranial surgery can be complex and high risk. Safety, ethical and financial factors make training in the area challenging. Head model 3-dimensional (3D) printing is a realistic training alternative to patient and traditional means of cadaver and animal model simulation.

OBJECTIVE

To describe important factors relating to the 3D printing of human head models and how such models perform as simulators.

METHODS

Searches were performed in PubMed, the Cochrane Library, Scopus, and Web of Science. Articles were screened independently by 3 reviewers using Covidence software. Data items were collected under 5 categories: study information; printers and processes; head model specifics; simulation and evaluations; and costs and production times.

RESULTS

Forty articles published over the last 10 years were included in the review. A range of printers, printing methods, and substrates were used to create head models and tissue types. Complexity of the models ranged from sections of single tissue type (e.g., bone) to high-fidelity integration of multiple tissue types. Some models incorporated disease (e.g., tumors and aneurysms) and artificial physiology (e.g., pulsatile circulation). Aneurysm clipping, bone drilling, craniotomy, endonasal surgery, and tumor resection were the most commonly practiced procedures. Evaluations completed by those using the models were generally favorable.

CONCLUSIONS

The findings of this review indicate that those who practice surgery and surgical techniques on 3D-printed head models deem them to be valuable assets in cranial surgery training. Understanding how surgical simulation on such models affects surgical performance and patient outcomes, and considering cost-effectiveness, are important future research endeavors.

Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy

CONTEXT

As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal?

OBJECTIVE

To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control.

EVIDENCE ACQUISITION

We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling.

EVIDENCE SYNTHESIS

We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes.

CONCLUSIONS

Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone.

PATIENT SUMMARY

We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.

Gesture Recognition in Robotic Surgery: A Review

OBJECTIVE

Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions.

METHODS

An article search was performed on 5 bibliographic databases with the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling.

RESULTS

A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches.

CONCLUSION

The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies.

SIGNIFICANCE

This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.

Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop "smart" training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the efficacy of such metrics: how do these metrics relate to clinical skill, and how to best compute these metrics from sensor data and relate them with similar metrics? This study addresses these questions in the context of hemodialysis cannulation by enrolling 52 clinicians who performed cannulation in a simulated arteriovenous (AV) fistula. For clinical skill, results demonstrate that the objective outcome metric flash ratio (FR), developed to measure the quality of task completion, outperformed traditional skill indicator metrics (years of experience and global rating sheet scores). For computing motion smoothness metrics for skill assessment, we observed that the lowest amount of smoothing could result in unreliable metrics. Furthermore, the relative efficacy of motion smoothness metrics when compared with other process metrics in correlating with skill was similar for FR, the most accurate measure of skill. These results provide guidance for the computation and use of motion-based metrics for clinical skill assessment, including utilizing objective outcome metrics as ideal measures for quantifying skill.

Assessing Laparoscopic Surgical Skills Using Similarity Network Models: A Pilot Study

Background: Medical devices are becoming more complex, and doctors need to learn quickly how to use new medical tools. However, it is challenging to objectively assess the fundamental laparoscopic surgical skill level and determine skill readiness for advancement. There is a lack of objective models to compare performance between medical trainees and experienced doctors. Methods: This article discusses the use of similarity network models for individual tasks and a combination of tasks to show the level of similarity between residents and medical students while performing each task and their overall laparoscopic surgical skill level using a medical device (eg laparoscopic instruments). When a medical student is connected to most residents, that student is competent to the next training level. Performance of sixteen participants (5 residents and 11 students) while performing 3 tasks in 3 different training schedules is used in this study. Results: The promising result shows the general positive progression of students over 4 training sessions. Our results also indicate that students with different training schedules have different performance levels. Students' progress in performing a task is quicker if the training sessions are held more closely compared to when the training sessions are far apart in time. Conclusions: This study provides a graph-based framework for evaluating new learners' performance on medical devices and their readiness for advancement. This similarity network method could be used to classify students' performance using similarity thresholds, facilitating decision-making related to training and progression through curricula.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set II: learning curve analysis

PURPOSE

The Johns Hopkins-Intuitive Gesture and Skill Assessment Working Set (JIGSAWS) dataset is used to develop robotic surgery skill assessment tools, but there has been no detailed analysis of this dataset. The aim of this study is to perform a learning curve analysis of the existing JIGSAWS dataset.

METHODS

Five trials were performed in JIGSAWS by eight participants (four novices, two intermediates and two experts) for three exercises (suturing, knot-tying and needle passing). Global Rating Scores and time, path length and movements were analyzed quantitatively and qualitatively by graphical analysis.

RESULTS

There are no significant differences in Global Rating Scale scores over time. Time in the suturing exercise and path length in needle passing had significant differences. Other kinematic parameters were not significantly different. Qualitative analysis shows a learning curve only for suturing. Cumulative sum analysis suggests completion of the learning curve for suturing by trial 4.

CONCLUSIONS

The existing JIGSAWS dataset does not show a quantitative learning curve for Global Rating Scale scores, or most kinematic parameters which may be due in part to the limited size of the dataset. Qualitative analysis shows a learning curve for suturing. Cumulative sum analysis suggests completion of the suturing learning curve by trial 4. An expanded dataset is needed to facilitate subset analyses.

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.

Current Trends in Artificial Intelligence Application for Endourology and Robotic Surgery

With the advent of electronic medical records and digitalization of health care over the past 2 decades, artificial intelligence (AI) has emerged as an enabling tool to manage complex datasets and deliver streamlined data-driven patient care. AI algorithms have the ability to extract meaningful signal from complex datasets through an iterative process akin to human learning. Through advancements over the past decade in deep learning, AI-driven innovations have accelerated applications in health care. Herein, the authors explore the development of these emerging AI technologies, focusing on the application of AI to endourology and robotic surgery.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software

Purpose

The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.

Methods

Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.

Results

Global rating scale scores correlated with skill in the knot-tying exercise (r = 0.55, p = 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (p < 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.

Conclusion

Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.

Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions

Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object-an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable.

Robotics-Assisted Surgical Skills Evaluation based on Electrocortical Activity

Skills assessment in Robotics-Assisted Minimally Invasive Surgery (RAMIS) is mainly performed based on temporal, motion-based and outcome-based metrics. While these components are essential for the proper assessment of skills in RAMIS, they do not suffice for full representation of all underlying aspects of skilled performance. Besides such commonplace components of skills, there exist other elements to be taken into account for comprehensive skills assessment. Among such elements are cognitive states (such as levels of stress, attention, concentration) that can directly affect performance. Investigating the impact of electrocortical activity and cognitive states of RAMIS surgeons over their performance has, however, received little attention in the literature. Therefore, in this paper, novel performance metrics based on electroencephalography (EEG) signals are studied for potential augmentation into RAMIS training and its assessment platform. For this purpose, a user study was conducted involving 23 novices and 9 expert RAMIS surgeons. The participants were asked to perform two tasks on the dv-Trainer®, (Mimic Technologies) RAMIS simulator, while their brain EEG signals were being measured using the Muse EEG headband (InteraXon Inc.). The performance metrics were defined as mean values of band powers of EEG signals over various ranges of frequency. Statistical analysis was performed to evaluate metrics over 5 different ranges of frequency for 4 electrode locations and during 2 RAMIS training tasks. The results indicated statistically significant differences in electrocortical activity between novices and experts in temporoparietal and left frontal regions of their brain for mid to high-frequency ranges. Overall, RAMIS experts showed lower levels of electrocortical activity in those regions compared to novices. The results indicate that electrocortical activity measured by EEG signals have the potential to provide useful information for skills assessment in RAMIS.

A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy

OBJECTIVES

To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes.

SUBJECTS AND METHODS

Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests.

RESULTS

Continence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively).

CONCLUSION

Using APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP.

Effect of surgeon experience and bony pelvic dimensions on surgical performance and patient outcomes in robot-assisted radical prostatectomy

OBJECTIVES

To evaluate the effects of surgeon experience, body habitus, and bony pelvic dimensions on surgeon performance and patient outcomes after robot-assisted radical prostatectomy (RARP).

PATIENTS, SUBJECTS AND METHODS

The pelvic dimensions of 78 RARP patients were measured on preoperative magnetic resonance imaging and computed tomography by three radiologists. Surgeon automated performance metrics (APMs [instrument motion tracking and system events data, i.e., camera movement, third-arm swap, energy use]) were obtained by a systems data recorder (Intuitive Surgical, Sunnyvale, CA, USA) during RARP. Two analyses were performed: Analysis 1, examined effects of patient characteristics, pelvic dimensions and prior surgeon RARP caseload on APMs using linear regression; Analysis 2, the effects of patient body habitus, bony pelvic measurement, and surgeon experience on short- and long-term outcomes were analysed by multivariable regression.

RESULTS

Analysis 1 showed that while surgeon experience affected the greatest number of APMs (P < 0.044), the patient's body mass index, bony pelvic dimensions, and prostate size also affected APMs during each surgical step (P < 0.043, P < 0.046, P < 0.034, respectively). Analysis 2 showed that RARP duration was significantly affected by pelvic depth (β = 13.7, P = 0.039) and prostate volume (β = 0.5, P = 0.024). A wider and shallower pelvis was less likely to result in a positive margin (odds ratio 0.25, 95% confidence interval [CI] 0.09-0.72). On multivariate analysis, urinary continence recovery was associated with surgeon's prior RARP experience (hazard ratio [HR] 2.38, 95% CI 1.18-4.81; P = 0.015), but not on pelvic dimensions (HR 1.44, 95% CI 0.95-2.17).

CONCLUSION

Limited surgical workspace, due to a narrower and deeper pelvis, does affect surgeon performance and patient outcomes, most notably in longer surgery time and an increased positive margin rate.

Objective Assessment of the Early Stages of the Learning Curve for the Senhance Surgical Robotic System

OBJECTIVE

The purpose of this research is to study the early stages of the Senhance learning curve to report how force feedback impacts learning rate. This serves as an exploratory investigation into assumptions that fellows and faculty will adjust faster to the Senhance in comparison with residents, and that force feedback will not hinder skill acquisition.

DESIGN

In this study, participants completed the peg transfer and precision cutting task from the Fundamentals of Laparoscopic Surgery (FLS) manual skills assessment five times each using the Senhance while instrument motion was tracked.

SETTING

This study took place in the Surgical Education and Activities Laboratory at Duke University Medical Center.

PARTICIPANTS

Participants for this study were residents, fellows, and faculty from Duke University Medical Center in general surgery and gynecology specialties (N = 16).

RESULTS

Postulated linear mixed effects models with participant level random effects showed significant improvement with additional attempts for the peg transfer task after adjusting for surgical experience and force feedback respectively for the primary FLS score metric. The secondary metric of total instrument path length also showed improvement (significant decreases) in path length with additional attempts after respectively adjusting for surgical experience and force feedback.

CONCLUSIONS

This study investigates the early stages of the learning curve of the Senhance. Exploratory modeling indicates that residents, fellows, and faculty surgeons rapidly adapt to the controls of the Senhance regardless of experience level and force feedback engagement. The results from this study may serve as motivation for future prospective studies that achieve sufficient statistical power with a larger sample size and strict experimental design.

Dynamic changes of brain functional states during surgical skill acquisition

There is lack of a standardized measure of technical proficiency and skill acquisition for robot-assisted surgery (RAS). Learning surgical skills, in addition to the interaction with the machine and the new surgical environment adds to the complexity of the learning process. Moreover, evaluation of surgeon performance in operating room is required to optimize patient safety. In this study, we investigated the dynamic changes of RAS trainee’s brain functional states by practice. We also developed brain functional state measurements to find the relationship between RAS skill acquisition (especially human-machine interaction skills) and reconfiguration of brain functional states. This relationship may help in providing trainees with helpful, structured feedback regarding skills requiring improvement and will help in tailoring training activities.

Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery

PURPOSE

With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge.

METHODS

We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels.

RESULTS

We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved competitive accuracies of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within a 1-3 second window, without needing an observation of entire training trial.

CONCLUSION

This study highlights the potential of deep architectures for efficient online skill assessment in modern surgical training.

Envisioning robotic surgery: Surgeons' needs and views on interacting with future technologies and interfaces

BACKGROUND

The development of technology in robotic surgery is typically presented from a technical perspective. This study considers the user perspective as an input to the development of technology by exploring potential solutions within and beyond the field of robotic surgery.

METHODS

Advanced technological solution concepts were selected based on a technology review and an ethnographic study. Using a future workshop method, these were rated and discussed by a group of surgeons from three perspectives: enhancing operation outcome, user experience and learning in the operating theatre.

RESULTS

Diverse technologies were considered to offer potential for supporting the surgeons' work. User experience and learning could be improved especially via solutions novel to robotic surgery. Robotic surgery technologies currently under development were mainly considered to support a good operation outcome. Suitability for practical work was elaborated upon, and related concerns were identified.

CONCLUSIONS

The results can support development of robotic surgery to enhance surgeons' work.

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills

Background. A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language. Methods. Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument. An artificial neural network (ANN) was trained to learn from experts and nonexperts’ behavior for pattern cutting task, whereas the assessment of transferring task was performed using a preestablished threshold. Four expert surgeons in laparoscopic surgery, from hospital “Raymundo Abarca Alarcón,” constituted the experienced class for the ANN. Sixteen trainees (10 medical students and 6 residents) without laparoscopic surgical skills and limited experience in minimal invasive techniques from School of Medicine at Universidad Autónoma de Guerrero constituted the nonexperienced class. Data from participants performing 5 daily repetitions for each task during 5 days were used to build the ANN. Results. The participants tend to improve their learning curve and dexterity with this laparoscopic training system. The classifier shows mean accuracy and receiver operating characteristic curve of 90.98% and 0.93, respectively. Moreover, the ANN was able to evaluate the psychomotor skills of users into 2 classes: experienced or nonexperienced. Conclusion. We constructed and evaluated an affordable laparoscopic trainer system using computer vision, augmented reality, and an artificial intelligence algorithm. The proposed trainer has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.

Getting ready for the first robotic prostatectomy, from basics to real practice

PURPOSE OF REVIEW

Robot-assisted radical prostatectomy (RARP) has been embraced by urologists and has become a treatment standard in many countries already. Learning how to perform a RARP is challenging and has not yet been standardized. The current review summarizes the latest concepts regarding the most effective way of training for RARP.

RECENT FINDINGS

The strategy to learn RARP should comprise didactic activities, skills lab training, participating in surgeries and mentorship. Skills lab and virtual simulators are valuable tools to develop manual abilities and to overcome the initial technical learning curve. Participating in surgeries is crucial for familiarization with the robot installation, steps of the surgical procedure and is essential for troubleshooting. Mentorship improves learning and is the safest way to initiate real practice. Innate and individual background variances were suggested to influence the learning process; however, there is paucity of robust evidence correlating previous surgical experience and, for example videogame playing with faster learning of RARP. Structured curricula were proposed to orient the training for robotic surgery; currently, only one is focused exclusively on urology.

SUMMARY

Systematic training is the most effective way to learn and surpass the possibly intense learning curve of RARP. Training activities should focus on developing cognitive and manual abilities. The existing curricula for robotic surgery training still require constant refinement; however, they offer good and structured guidance to train for RARP.

Moving from Novice to Expertise and Its Implications for Instruction

Objective: To address the stages of expertise development, what differentiates a novice from an expert, and how the development and differences impact how we teach our classes or design the curriculum. This paper will also address the downside of expertise and discuss the importance of teaching expertise relative to domain expertise. Summary: Experts develop through years of experience and by progressing from novice, advance beginner, proficient, competent, and finally expert. These stages are contingent on progressive problem solving, which means individuals must engage in increasingly complex problems, strategically aligned with the learner's stage of development. Thus, several characteristics differentiate experts from novices. Experts know more, their knowledge is better organized and integrated, they have better strategies for accessing knowledge and using it, and they are self-regulated and have different motivations.

Motion control skill assessment based on kinematic analysis of robotic end-effector movements

BACKGROUND

The performance of robotic end-effector movements can reflect the user's operation skill difference in robot-assisted minimally invasive surgery. This study quantified the trade-off of speed-accuracy-stability by kinematic analysis of robotic end-effector movements to assess the motion control skill of users with different levels of experience.

METHODS

Using 'MicroHand S' system, 10 experts, 10 residents and 10 novices performed single-hand test and bimanual coordination test. Eight metrics based on the movements of robotic end-effectors were applied to evaluate the users' performance.

RESULTS

In the single-hand test, experts outperformed other groups except for movement speed; in the bimanual coordination test, experts also performed better except for movement time and movement speed. No statistically significant difference in performance was found between residents and novices.

CONCLUSIONS

The kinematic differences obtained from the movements of robotic end-effectors can be applied to assess the motion control skill of users with different skill levels.

Automated robot-assisted surgical skill evaluation: Predictive analytics approach

BACKGROUND

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.

METHODS

Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied.

RESULTS

The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.

CONCLUSION

This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.

Surgical Skill Assessment Using Motion Quality and Smoothness

OBJECTIVES

This article presents a quantitative technique to assess motion quality and smoothness during the performance of micromanipulation tasks common to surgical maneuvers. The objective is to investigate the effectiveness of the jerk index, a derivative of acceleration with respect to time, as a kinetostatic measure for assessment of surgical performance.

DESIGN

A surgical forceps was instrumented with a position tracker and accelerometer that allowed measurement of position and acceleration relative to tool motion. Participants were asked to perform peg-in-hole tasks on a modified O'Connor Dexterity board and a Tweezer Dexterity pegboard (placed inside a skull). Normalized jerk index was calculated for each individual task to compare smoothness of each group.

SETTING

This study was conducted at Project neuroArm, Cumming School of Medicine, the University of Calgary.

PARTICIPANTS

Four groups of participants (surgeons, surgery residents, engineers, and gamers) participated in the tests.

RESULTS

Results showed that the surgeons exhibited better jerk index performance in all tasks. Moreover, the residents experienced motions closer to the surgeons compared to the engineers and gamers. One-way analysis of variance test indicated a significant difference between the mean values of normalized jerk indices among 4 groups during the performance of all tasks. Moreover, the mean value of the normalized jerk index significantly varied for each group from one task to another.

CONCLUSIONS

Normalized jerk index as an independent parameter with respect to time and amplitude is an indicator of motion smoothness and can be used to assess hand motion dexterity of surgeons. Furthermore, the method provides a quantifiable metrics for trainee assessment and proficiency, particularly relevant as surgical training shifts toward a competency-based paradigm.

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

OBJECTIVE

State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

METHODS

In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems.

RESULTS

Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings.

CONCLUSION

Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons.

SIGNIFICANCE

The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.

Automated video-based assessment of surgical skills for training and evaluation in medical schools

PURPOSE

Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches however are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities.

METHOD

We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis.

RESULTS

We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos.

CONCLUSION

Our evaluations show that frequency features perform better than motion texture features, which in-turn perform better than symbol-/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

Viewpoint matters: objective performance metrics for surgeon endoscope control during robot-assisted surgery

Background

Effective visualization of the operative field is vital to surgical safety and education. However, additional metrics for visualization are needed to complement other common measures of surgeon proficiency, such as time or errors. Unlike other surgical modalities, robot-assisted minimally invasive surgery (RAMIS) enables data-driven feedback to trainees through measurement of camera adjustments. The purpose of this study was to validate and quantify the importance of novel camera metrics during RAMIS.

Methods

New (n = 18), intermediate (n = 8), and experienced (n = 13) surgeons completed 25 virtual reality simulation exercises on the da Vinci Surgical System. Three camera metrics were computed for all exercises and compared to conventional efficiency measures.

Results

Both camera metrics and efficiency metrics showed construct validity (p < 0.05) across most exercises (camera movement frequency 23/25, camera movement duration 22/25, camera movement interval 19/25, overall score 24/25, completion time 25/25). Camera metrics differentiated new and experienced surgeons across all tasks as well as efficiency metrics. Finally, camera metrics significantly (p < 0.05) correlated with completion time (camera movement frequency 21/25, camera movement duration 21/25, camera movement interval 20/25) and overall score (camera movement frequency 20/25, camera movement duration 19/25, camera movement interval 20/25) for most exercises.

Conclusions

We demonstrate construct validity of novel camera metrics and correlation between camera metrics and efficiency metrics across many simulation exercises. We believe camera metrics could be used to improve RAMIS proficiency-based curricula.

Evaluating Robotic Surgical Skills Performance Under Distractive Environment Using Objective and Subjective Measures

Background. Distractions are recognized as a significant factor affecting performance in safety critical domains. Although operating rooms are generally full of distractions, the effect of distractions on robot-assisted surgical (RAS) performance is unclear. Our aim was to investigate the effect of distractions on RAS performance using both objective and subjective measures. Methods. Fifteen participants performed a knot-tying task using the da Vinci Surgical System and were exposed to 3 distractions: (1) passive distraction entailed listening to noise with a constant heart rate, (2) active distraction included listening to noise and acknowledging a change of random heart rate from 60 to 120 bpm, and (3) interactive distraction consisted of answering math questions. The objective kinematics of the surgical instrument tips were used to evaluate performance. Electromyography (EMG) of the forearm and hand muscles of the participants were collected. The median EMG frequency (EMGfmed) and the EMG envelope (EMGenv) were analyzed. NASA Task Load Index and Fundamentals of Laparoscopic Surgery score were used to evaluate the subjective performance. One-way repeated analysis of variance was applied to examine the effects of distraction on skills performance. Spearman’s correlations were conducted to compare objective and subjective measures. Results. Significant distraction effect was found for all objective kinematics measures ( P < .05). There were significant distraction effects for EMG measures (EMGenv, P < .004; EMGfmed, P = .031). Significant distraction effects were also found for subjective measurements. Conclusions. Distraction impairs surgical skills performance and increases muscle work. Understanding how the surgeons cope with distractions is important in developing surgical education.

Robot-assisted surgery: an emerging platform for human neuroscience research

Classic studies in human sensorimotor control use simplified tasks to uncover fundamental control strategies employed by the nervous system. Such simple tasks are critical for isolating specific features of motor, sensory, or cognitive processes, and for inferring causality between these features and observed behavioral changes. However, it remains unclear how these theories translate to complex sensorimotor tasks or to natural behaviors. Part of the difficulty in performing such experiments has been the lack of appropriate tools for measuring complex motor skills in real-world contexts. Robot-assisted surgery (RAS) provides an opportunity to overcome these challenges by enabling unobtrusive measurements of user behavior. In addition, a continuum of tasks with varying complexity-from simple tasks such as those in classic studies to highly complex tasks such as a surgical procedure-can be studied using RAS platforms. Finally, RAS includes a diverse participant population of inexperienced users all the way to expert surgeons. In this perspective, we illustrate how the characteristics of RAS systems make them compelling platforms to extend many theories in human neuroscience, as well as, to develop new theories altogether.